Common Interview Questions

Easy

What is a load balancer, and why is it used in system design?

A load balancer distributes incoming network traffic across multiple servers to ensure no single server becomes overwhelmed. This improves the availability and scalability of a system. By balancing the load, it reduces the risk of failure due to overload. Load balancers can also perform health checks on servers to ensure they are operational. They can be implemented as hardware or software solutions.

What is horizontal scaling, and how does it differ from vertical scaling?

Horizontal scaling involves adding more servers to a system to handle increased load, while vertical scaling means upgrading the existing server’s hardware. Horizontal scaling is often preferred for distributed systems as it improves fault tolerance and scalability. Vertical scaling has hardware limitations and can become costly. Horizontal scaling typically requires designing stateless services to work effectively.

What is the difference between SQL and NoSQL databases?

SQL databases are relational and use structured query language for defining and managing data. They are best suited for structured data and support ACID transactions. NoSQL databases are non-relational and are optimized for unstructured data and high scalability. Examples include key-value stores, document stores, and graph databases. SQL databases excel in consistency, while NoSQL is preferred for scalability and flexibility.

What is caching, and how does it improve system performance?

Caching is the process of storing frequently accessed data in a temporary storage layer to reduce latency. It improves performance by avoiding repeated computations or database queries. Popular caching strategies include in-memory caches like Redis and Memcached. Caching can be applied at various levels, such as database queries, API responses, or computed results. Effective caching strategies should consider eviction policies to manage storage limits.

What is a CDN, and how does it help in system design?

A Content Delivery Network (CDN) is a distributed network of servers that deliver content to users based on their geographic location. CDNs improve the speed and reliability of content delivery by caching static assets like images and videos. They reduce latency by serving content from servers closer to the user. CDNs also reduce the load on origin servers by handling a large portion of traffic. Examples of CDNs include Cloudflare and Akamai.

What is the role of a database index in system design?

A database index is a data structure that improves the speed of data retrieval operations. It works like a table of contents in a book, allowing quick access to specific rows in a database. Indexes are typically created on frequently queried columns. However, they add overhead to write operations because the index needs to be updated. Proper indexing can significantly enhance the performance of read-heavy systems.

What is a monolithic architecture?

Monolithic architecture is a software design pattern where the entire application is built as a single, unified codebase. It includes all components such as the frontend, backend, and database access. While it is simple to develop and deploy initially, it becomes difficult to scale and maintain as the application grows. Failures in one part of the application can affect the entire system. Migrating to microservices is often a solution to overcome its limitations.

What is the CAP theorem in distributed systems?

The CAP theorem states that a distributed system can only achieve two out of three properties: Consistency, Availability, and Partition Tolerance. Consistency ensures all nodes see the same data at the same time. Availability guarantees the system remains operational even if some nodes fail. Partition Tolerance allows the system to function despite network partitions. Trade-offs must be made based on system requirements.

What are microservices, and how do they differ from monolithic architecture?

Microservices are a software design approach where applications are composed of small, independent services that communicate over APIs. Each microservice focuses on a specific business function and can be developed, deployed, and scaled independently. This contrasts with monolithic architecture, where all functionalities are tightly coupled in a single codebase. Microservices provide better fault isolation and flexibility. However, they add complexity to system management and communication.

What is the purpose of a message queue in system design?

A message queue facilitates asynchronous communication between different components of a system. Producers send messages to the queue, and consumers process them at their own pace. This decouples services, improving scalability and fault tolerance. Popular message queues include RabbitMQ, Apache Kafka, and AWS SQS. They are commonly used in systems requiring event-driven architectures or background job processing.

Medium

How would you design a URL shortening service like Bitly?

A URL shortening service converts long URLs into shorter ones for easier sharing and tracking. The system should support generating unique short URLs using hash functions or key-generation strategies. A database is needed to store mappings between short and original URLs. Caching can be used to reduce read latency for frequently accessed URLs. To ensure scalability, the design should consider load balancing and partitioning the database.

What are the key components of a distributed file system like HDFS?

A distributed file system like HDFS consists of a NameNode and DataNodes. The NameNode manages metadata and directory structure, while DataNodes store the actual file data. Files are divided into blocks and replicated across multiple DataNodes for fault tolerance. A heartbeat mechanism ensures the health of DataNodes. Clients communicate with the NameNode for metadata and directly with DataNodes for file access.

How would you design a rate limiter for an API?

A rate limiter prevents users from making excessive API requests within a specific time frame. Common algorithms include token bucket and sliding window. The design should involve tracking user requests in a datastore like Redis. Requests beyond the limit should return a 429 status code. To ensure high performance, rate limiting logic can be implemented at the edge using reverse proxies like NGINX or CDNs.

What is sharding, and how does it improve database scalability?

Sharding is the process of dividing a database into smaller, more manageable pieces called shards. Each shard holds a subset of the database’s data, typically distributed based on a shard key. This allows the database to handle more traffic by distributing queries across multiple servers. Sharding improves read and write performance and enables horizontal scaling. However, it adds complexity in managing and querying the shards.

A notification system should handle real-time and batch notifications efficiently. It requires a database to store user preferences and notifications, a message queue for asynchronous processing, and workers to send notifications via different channels. Real-time notifications can use WebSockets or push notifications. To ensure scalability, the system should decouple producers (e.g., activity services) from consumers (e.g., notification services). Caching frequently accessed notifications can enhance performance.

What is eventual consistency, and when is it used in system design?

Eventual consistency is a consistency model where updates to a distributed system are guaranteed to propagate to all nodes eventually. It sacrifices immediate consistency for higher availability and partition tolerance. Systems like DynamoDB and Cassandra adopt this model for scalability. It is suitable for applications where immediate consistency is not critical, such as social media feeds or analytics systems. Developers must design around potential data conflicts.

How would you design a real-time chat application?

A real-time chat application requires WebSockets for persistent bi-directional communication between clients and the server. A message queue can handle message delivery to ensure reliability. A database is needed to store chat history, while caching can improve the retrieval of recent messages. To support scalability, messages can be sharded by chat rooms or user IDs. Load balancers and horizontal scaling of chat servers are essential for handling high traffic.

What is the purpose of a reverse proxy in system design?

A reverse proxy sits in front of servers, intercepting client requests and forwarding them to appropriate backend services. It provides benefits such as load balancing, caching, and SSL termination. Reverse proxies also enhance security by hiding backend servers from direct access. Tools like NGINX and HAProxy are commonly used as reverse proxies. They play a crucial role in distributing traffic and improving system resilience.

How would you design a logging and monitoring system for a large-scale application?

A logging and monitoring system should aggregate logs from all application components into a centralized system like Elasticsearch or Splunk. Metrics should be collected using tools like Prometheus and visualized with Grafana. Alerts can be set up for anomalies using thresholds or machine learning models. Logs and metrics should be retained for an appropriate period for troubleshooting. Scalability and fault tolerance are crucial for ensuring uninterrupted monitoring.

What is a distributed transaction, and how can it be implemented?

A distributed transaction spans multiple nodes or services and ensures consistency across them. Implementing distributed transactions requires protocols like Two-Phase Commit (2PC) or distributed consensus algorithms like Paxos or Raft. Alternatively, systems can use eventual consistency with compensation mechanisms in a Saga pattern. Distributed transactions are necessary for scenarios like distributed database operations or multi-service updates. Their complexity often requires careful trade-offs between consistency and availability.

Hard

How would you design a search engine like Google?

A search engine requires web crawlers to index content from the web and a database to store the indexed data. The index should be optimized for fast retrieval using data structures like inverted indexes. Query processing involves ranking results based on relevance using algorithms like PageRank. Scalability is achieved through sharding and distributed storage systems. Additional features like autocomplete and spell correction enhance user experience.

How would you design a payment processing system like PayPal?

A payment system requires secure user authentication and transaction handling. A database stores user accounts and transaction records, while an external payment gateway processes payments. It should support multiple currencies and payment methods. The design must prioritize security, using encryption for sensitive data and monitoring for fraud detection. Scalability can be achieved using microservices for different modules like payments, refunds, and notifications.

How would you design a recommendation system like Netflix?

A recommendation system uses machine learning algorithms to suggest content to users. It requires user behavior data, such as viewing history and ratings, and metadata about content. Collaborative filtering or content-based filtering can be used, often enhanced with deep learning. A caching layer ensures low-latency responses for recommendations. Scalability is critical, as the system must handle millions of users simultaneously.

How would you design a distributed cache like Redis?

A distributed cache stores frequently accessed data across multiple nodes to improve response times. The system should support sharding and replication for scalability and fault tolerance. A consensus mechanism ensures consistency across nodes in case of updates. LRU (Least Recently Used) or LFU (Least Frequently Used) eviction policies manage memory constraints. Advanced features like persistence and clustering enhance reliability.

How would you design a video streaming platform like YouTube?

A video streaming platform requires a content delivery network (CDN) for efficient video distribution. Videos are transcoded into multiple resolutions to support various devices and bandwidths. Metadata and user preferences power search and recommendation features. The backend must handle video uploads, storage, and streaming, ensuring scalability. Monitoring and adaptive bitrate streaming enhance the user experience.

What is a distributed consensus algorithm, and how does it work?

Distributed consensus algorithms ensure that multiple nodes in a system agree on a shared state, even in the presence of failures. Examples include Paxos and Raft. These algorithms use mechanisms like leader election and log replication to maintain consistency. They are essential for systems like distributed databases and blockchains. However, achieving consensus can be slow due to network delays and fault tolerance requirements.

How would you design a large-scale email service like Gmail?

A large-scale email service requires robust user authentication and scalable storage for email data. Emails are processed through a mail transfer agent (MTA) and stored using distributed file systems. Features like spam filtering and search rely on machine learning and indexing. The service should ensure high availability using replication and load balancing. Real-time notifications for new emails are achieved using push mechanisms.

How would you handle database schema changes in a live system?

Schema changes in a live system should be performed incrementally to minimize disruptions. Start by adding new columns or tables without modifying existing structures. Migrate data gradually and update application code to use the new schema. Once all dependencies are updated, remove old schema components. This process, known as online schema migration, often involves tools like Flyway or Liquibase.

How would you design a blockchain-based system?

A blockchain-based system maintains a decentralized, immutable ledger across nodes. Transactions are grouped into blocks, which are validated using consensus mechanisms like Proof of Work or Proof of Stake. Blocks are linked using cryptographic hashes to ensure tamper resistance. Smart contracts enable programmability within the blockchain. Scalability remains a challenge, addressed using techniques like sharding or Layer 2 solutions.

How would you design a system to handle real-time analytics for IoT data?

A real-time analytics system for IoT data requires a data ingestion layer to handle high-velocity streams. Tools like Apache Kafka can be used for messaging, while a stream processing framework like Apache Flink or Spark processes the data. Results are stored in a database optimized for time-series data, such as InfluxDB. Dashboards provide visualization, and alerts notify anomalies. Scalability and fault tolerance are critical for handling massive IoT data streams.